Healthcare workers’ priorities of WHO snakebite strategic objectives for the control and prevention of snakebite envenoming in the Eastern Region of Ghana: A machine learning statistical design of experiment modeling

Eric Nyarko, Iddrisu Abugbil Atubiga, Emmanuel Tetteh Siame, José María Gutiérrez and Eduardo Alberto Fernandez

PLOS Neglected Tropical Diseases, 2025, vol. 19, issue 7, 1-16

Abstract: Background: Snakebite is a severe neglected tropical disease (NTD) that affects 2.5 million people each year, resulting in the deaths of 81,000–138,000 individuals, including rural villagers, agricultural workers, and children. The World Health Organization (WHO) has set strategic objectives to halve the deaths and disabilities caused by snakebite envenoming (SBE) by 2030. This study used innovative research methods, such as the statistical design of experiments and machine learning (ML), to explore healthcare workers’ priorities in Ghana regarding the WHO’s strategic objectives for controlling and preventing SBE. The goal was to identify their priority needs to guide the development of a research agenda and relevant interventions or policies that prioritize local needs while aligning with the WHO’s strategic objectives for SBE control and prevention. Method: In this cross-sectional study, we employed a MaxDiff statistical design to collect data on the prioritization of the WHO strategic objectives for SBE from 137 healthcare workers in the Kwahu Afram Plains North and South districts of the Eastern Region of Ghana from August to December 2024. We divided the final dataset using a hold-back validation method, maintaining a training-to-validation ratio of 70:30. For data analysis, we utilized a diverse range of five machine learning models: Ridge Regression, Elastic Net, LASSO, a Generalized Regression Model with Pruned Forward Selection, and Forward Selection. To compare the performance of these models, we used several key metrics, including Akaike Information Criterion corrected (AICc), the Bayesian Information Criterion (BIC), the Root Average Squared Error (RASE), negative log-likelihood, and the total time taken to fit each model. Results: The Ridge regression model appeared as the best candidate among the ML models used in this study. Its superior predictive performance justifies the computational cost it requires, making it the preferred option for applications that prioritize both predictive performance and computational efficiency. This model consistently predicted key WHO strategic objectives for preventing and controlling SBE. Of the objectives, ‘Ensuring safe and effective treatment’ had the highest priority, followed by ‘Strengthening health systems’, ‘Empowering and engaging communities’ and ‘Increasing partnerships, coordination, and resources’. This underscores their order of importance for local initiatives. Therefore, these strategies must be prioritized when designing local policies, relevant interventions, and research agendas. Conclusion: By utilizing a MaxDiff statistical experiment design and five machine learning models, participants prioritized the WHO strategic objectives for preventing and controlling SBE in Ghana. Our findings provide essential insights into local policy-making and intervention strategies and for shaping research agendas in Ghana. A local action plan is urgently needed, prioritizing ‘Ensuring safe and effective treatment’ at the community level, followed by ‘Strengthening health systems’, ‘Empowering and engaging communities’, and ‘Increasing partnerships, coordination, and resources’. Prioritizing these strategies in Ghana is crucial for supporting the WHO’s goal of reducing the global SBE burden by 50% by 2030. The success of these strategies hinges on the active involvement of the Ministry of Health and the Ghana Health Service in their implementation at the local level and within the health system. Author summary: The WHO has set ambitious strategic objectives to halve the deaths and disabilities caused by SBE by 2030. However, significant challenges exist in sub-Saharan Africa, including a lack of rigorous research evidence and a misalignment of research with local priorities. Comprehensive research is essential to identifying country-level priorities to guide the design and implementation of locally relevant plans for combating this NTD. Achieving this goal demands innovative research methods, including expanding the traditional health research toolkit by adapting existing methodologies and incorporating new approaches to gather improved information on snakebites in Ghana and the sub-region. To this end, this study employed innovative research methods, including the statistical design of experiments and artificial intelligent/machine learning models, to identify healthcare workers’ priorities regarding the WHO’s strategic objectives for controlling and preventing SBE. Our findings indicate that healthcare workers in a particular region of Ghana prioritized ‘Ensuring safe and effective treatment’, followed by ‘Strengthening health systems’, ‘Empowering and engaging communities’, and ‘Increasing partnerships, coordination, and resources’. Implementing a local action plan, as our research suggests, has the potential to significantly impact the health landscape, offering hope for communities at risk of snakebite and snakebite victims. Prioritizing these strategies in Ghana is crucial for supporting the WHO’s goal of reducing the global SBE burden by 50% by 2030. However, the success of these strategies hinges on the active involvement of the Ministry of Health and the Ghana Health Service in their implementation at the local level and within the health system.

Date: 2025
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DOI: 10.1371/journal.pntd.0013295

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